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Artificial Intelligence: Knowledge Representation and Reasoning

Artificial Intelligence: Knowledge Representation and Reasoning. Instructor: Prof. Deepak Khemani, Department of Computer Science and Engineering, IIT Madras. An intelligent agent needs to be able to solve problems in its world. The ability to create representations of the domain of interest and reason with these representations is a key to intelligence. In this course we explore a variety of representation formalisms and the associated algorithms for reasoning. We start with a simple language of propositions, and move on to first order logic, and then to representations for reasoning about action, change, situations, and about other agents in incomplete information situations. (from nptel.ac.in)

Lecture 22 - Skolemization

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Introduction

Lecture 01 - Introduction

Lecture 02 - Introduction to Knowledge Representation and Reasoning

Lecture 03 - An Introduction to Formal Logics

Lecture 04 - Propositional Logic: Language, Semantics and Reasoning

Lecture 05 - Propositional Logic: Syntax and Truth Values

Propositional Logic

Lecture 06 - Valid Arguments and Proof Systems

Lecture 07 - Rules of Inference and Natural Deduction

Lecture 08 - Axiomatic Systems and Hilbert Style Proofs

Lecture 09 - The Tableau Method

Lecture 10 - The Resolution Refutation Method

First Order Logic

Lecture 11 - Syntax

Lecture 12 - Semantics

Lecture 13 - Entailment and Models

Lecture 14 - Proof Systems

Lecture 15 - Forward Chaining

Lecture 16 - Unification

Rule Based Systems

Lecture 17 - Forward Chaining Rule Based Systems

Lecture 18 - The Rete Algorithm

Lecture 19 - The Rete Algorithm: Example

Lecture 20 - Programming in a Rule Based Language

Lecture 21 - The OPS5 Expert System Shell

Representation in First Order Logic

Lecture 22 - Skolemization

Lecture 23 - Terminological Facts

Lecture 24 - Properties and Categories

Lecture 25 - Reification and Abstract Entities

Lecture 26 - Resource Description Framework (RDF)

Lecture 27 - The Event Calculus: Reasoning about Change

Natural Language Understanding

Lecture 28 - Natural Language Semantics

Lecture 29 - Conceptual Dependency (CD) Theory

Lecture 30 - Conceptual Dependency (CD) Theory (cont.)

Lecture 31 - English to CD Theory

Logic Programming with Prolog

Lecture 32 - Backward Chaining

Lecture 33 - Logic Programming

Lecture 34 - Prolog

Lecture 35 - Search in Prolog

Lecture 36 - Controlling Search

Lecture 37 - The Cut Operator in Prolog

Resolution Refutation in First Order Logic

Lecture 38 - Incompleteness

Lecture 39 - The Resolution Method for First Order Logic

Lecture 40 - Clause Form

Lecture 41 - First Order Logic with Equality

Lecture 42 - Complexity of Resolution Refutation

Knowledge Structures

Lecture 43 - Semantic Nets and Frames

Lecture 44 - Scripts

Lecture 45 - Applying Scripts

Lecture 46 - Goals, Plans and Actions

Lecture 47 - Plan Applier Mechanism

Lecture 48 - Top Down and Bottom Up Reasoning

Description Logic

Lecture 49 - Introduction

Lecture 50 - Normalisation

Lecture 51 - Structure Matching

Lecture 52 - Structure Matching: Example

Lecture 53 - Classification

Lecture 54 - A-Box Reasoning

Description Logic and Inheritance

Lecture 55 - Description Logic: Extensions

Lecture 56 - Description Logic: ALC

Lecture 57 - ALC Examples

Lecture 58 - Taxonomies and Inheritance

Lecture 59 - Beliefs

Lecture 60 - Inheritance Hierarchies

Default Reasoning

Lecture 61 - Introduction

Lecture 62 - Circumscription

Lecture 63 - Circumscription (cont.)

Lecture 64 - Minimal Models

Lecture 65 - Event Calculus Revisited

Lecture 66 - Circumscription in Event Calculus

Epistemic Logic

Lecture 67 - Default Logic

Lecture 68 - Autoepistemic Logic

Lecture 69 - Epistemic Logic

Lecture 70 - The Muddy Children Puzzle